Analysis date: 2023-08-08

Depends on

CRC_Xenografts_Batch2_DataProcessing Script

load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")

TODO

Setup

Load libraries and functions

Analysis

DEP

Tyrosine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set1, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set1_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## Loading required namespace: reactome.db
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway      pval
## 1:                                          2-LTR circle formation 0.7889344
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.7029126
## 3:                                       ABC transporter disorders 0.5264484
## 4:                          ABC-family proteins mediated transport 0.5264484
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.4951456
## 6:                       ADP signalling through P2Y purinoceptor 1 0.5532995
##         padj    log2err         ES        NES size
## 1: 0.9690732 0.05773085  0.6088710  0.8140883    1
## 2: 0.9653366 0.06064040 -0.6431452 -0.8637563    1
## 3: 0.9653366 0.05389790 -0.4803437 -0.9795159    8
## 4: 0.9653366 0.05389790 -0.4803437 -0.9795159    8
## 5: 0.9653366 0.07808923 -0.7459677 -1.0018490    1
## 6: 0.9653366 0.06553210 -0.6573884 -0.9718021    2
##                       leadingEdge
## 1:                           2547
## 2:                           6385
## 3: 5687,5683,7415,10213,5696,5692
## 4: 5687,5683,7415,10213,5696,5692
## 5:                           5575
## 6:                           1432
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set1, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway      pval
## 1:                                          2-LTR circle formation 0.9883495
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.7300971
## 3:                                       ABC transporter disorders 0.9818781
## 4:                          ABC-family proteins mediated transport 0.9818781
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.3340122
## 6:                       ADP signalling through P2Y purinoceptor 1 0.6293103
##         padj    log2err         ES        NES size
## 1: 0.9941690 0.04486451 -0.5020161 -0.6775977    1
## 2: 0.9789094 0.05884382 -0.6330645 -0.8544806    1
## 3: 0.9941690 0.03761426 -0.2167689 -0.4513799    8
## 4: 0.9941690 0.03761426 -0.2167689 -0.4513799    8
## 5: 0.9361785 0.10319747  0.8366935  1.1263557    1
## 6: 0.9361785 0.07113274  0.5955884  0.9113494    2
##                           leadingEdge
## 1:                               2547
## 2:                               6385
## 3: 5696,7415,5687,5692,5683,10213,...
## 4: 5696,7415,5687,5692,5683,10213,...
## 5:                               5575
## 6:                               6714

## Note: Row-scaling applied for this heatmap

Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set1, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.
##                                                            pathway      pval
## 1:                                          2-LTR circle formation 0.8011696
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.5107212
## 3:                                       ABC transporter disorders 0.7048458
## 4:                          ABC-family proteins mediated transport 0.7048458
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.2865497
## 6:                       ADP signalling through P2Y purinoceptor 1 0.7500000
##         padj    log2err        ES       NES size
## 1: 0.9529068 0.05468085 0.6028226 0.8076988    1
## 2: 0.8661945 0.07667469 0.7439516 0.9967922    1
## 3: 0.8934204 0.10473282 0.3190184 0.8182498    8
## 4: 0.8934204 0.10473282 0.3190184 0.8182498    8
## 5: 0.7166461 0.11012226 0.8608871 1.1534695    1
## 6: 0.9222656 0.06508776 0.4956529 0.7826204    2
##                           leadingEdge
## 1:                               2547
## 2:                               6385
## 3: 5683,5691,5692,10213,5706,5696,...
## 4: 5683,5691,5692,10213,5706,5696,...
## 5:                               5575
## 6:                               6714
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

EC vs E

data_diff_EC_vs_E_pY <- test_diff(pY_se_Set1, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway       pval
## 1:                                          2-LTR circle formation 0.75855131
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.79051383
## 3:                                       ABC transporter disorders 0.24154589
## 4:                          ABC-family proteins mediated transport 0.24154589
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.07905138
## 6:                       ADP signalling through P2Y purinoceptor 1 0.27563025
##         padj    log2err         ES        NES size
## 1: 0.9129258 0.05871859 -0.6310484 -0.8313235    1
## 2: 0.9137781 0.05594286  0.5967742  0.8107522    1
## 3: 0.6504293 0.09139243  0.5582638  1.1868006    8
## 4: 0.6504293 0.09139243  0.5582638  1.1868006    8
## 5: 0.5667416 0.22496609  0.9616935  1.3065162    1
## 6: 0.6504293 0.10319747  0.7676768  1.1662680    2
##                       leadingEdge
## 1:                           2547
## 2:                           6385
## 3: 5683,5687,10213,5692,7415,5691
## 4: 5683,5687,10213,5692,7415,5691
## 5:                           5575
## 6:                      1432,6714

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set1, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set1_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway      pval
## 1:                                          2-LTR circle formation 0.7137255
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.1509804
## 3:                                       ABC transporter disorders 0.7577093
## 4:                          ABC-family proteins mediated transport 0.7577093
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.7274510
## 6:                       ADP signalling through P2Y purinoceptor 1 0.1886477
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.9779589 0.06037864  0.6350806  0.8540177    1        2547
## 2: 0.6337330 0.15851411  0.9274194  1.2471370    1        6385
## 3: 0.9779589 0.10027911  0.2658487  0.7583154    8   5696,5683
## 4: 0.9779589 0.10027911  0.2658487  0.7583154    8   5696,5683
## 5: 0.9779589 0.05947603  0.6270161  0.8431731    1        5575
## 6: 0.7175283 0.12814292 -0.7711795 -1.2147540    2        1432
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

#data_results <- get_df_long(dep)

Serine/Threonine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pST <- test_diff(pST_se_Set1, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_ctrl_vs_E_pST <- add_rejections_SH(data_diff_ctrl_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pST, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set1_form, dep_ctrl_vs_E_pST, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                    pathway        pval
## 1:                                  2-LTR circle formation 0.007713272
## 2:                               ABC transporter disorders 0.630434783
## 3:                  ABC-family proteins mediated transport 0.569264069
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.048096663
## 5:               ADP signalling through P2Y purinoceptor 1 0.964426877
## 6:               AKT phosphorylates targets in the cytosol 0.098148148
##         padj    log2err         ES        NES size    leadingEdge
## 1: 0.6413673 0.40701792  0.9972973  1.3250367    1           3159
## 2: 0.9889853 0.06674261 -0.6810811 -0.9197246    1           5684
## 3: 0.9889853 0.07647671  0.6147864  0.9529845    2        23,5684
## 4: 0.6413673 0.32177592  0.8144864  1.4475812    3 5576,5577,5573
## 5: 0.9933804 0.04678830 -0.5162162 -0.6970929    1           5321
## 6: 0.7814707 0.19381330 -0.8827569 -1.3271503    2          84335
data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set1, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                    pathway        pval
## 1:                                  2-LTR circle formation 0.005040512
## 2:                               ABC transporter disorders 0.545267490
## 3:                  ABC-family proteins mediated transport 0.892778993
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.197560976
## 5:               ADP signalling through P2Y purinoceptor 1 0.299227799
## 6:               AKT phosphorylates targets in the cytosol 0.084403670
##         padj    log2err         ES        NES size    leadingEdge
## 1: 0.5466665 0.40701792  0.9972973  1.3386123    1           3159
## 2: 0.9960152 0.07608372 -0.7162162 -0.9648626    1           5684
## 3: 0.9978247 0.05502111  0.4139129  0.6440246    2             23
## 4: 0.9960152 0.15419097  0.7360016  1.3030981    3 5576,5577,5573
## 5: 0.9960152 0.10672988  0.8554054  1.1481593    1           5321
## 6: 0.9960152 0.20895503 -0.8899015 -1.3279377    2          84335

## Note: Row-scaling applied for this heatmap

Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set1, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                    pathway       pval      padj
## 1:                                  2-LTR circle formation 0.02087899 0.8251450
## 2:                               ABC transporter disorders 0.64624506 0.9848593
## 3:                  ABC-family proteins mediated transport 0.77828746 0.9848593
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.03051579 0.8251450
## 5:               ADP signalling through P2Y purinoceptor 1 0.20481928 0.9848593
## 6:               AKT phosphorylates targets in the cytosol 0.20030581 0.9848593
##       log2err         ES        NES size    leadingEdge
## 1: 0.35248786  0.9918919  1.3367314    1           3159
## 2: 0.06553210 -0.6810811 -0.9096888    1           5684
## 3: 0.04486451 -0.5331529 -0.8011377    2        5684,23
## 4: 0.35248786  0.8550136  1.6044105    3 5576,5577,5573
## 5: 0.13574094  0.8959459  1.2074290    1           5321
## 6: 0.11776579 -0.8099516 -1.2170670    2          84335

EC vs E

data_diff_EC_vs_E_pST <- test_diff(pST_se_Set1, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                    pathway      pval      padj
## 1:                                  2-LTR circle formation 0.1768173 0.7943790
## 2:                               ABC transporter disorders 0.9194499 0.9754768
## 3:                  ABC-family proteins mediated transport 0.3558648 0.8561713
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.7941748 0.9673784
## 5:               ADP signalling through P2Y purinoceptor 1 0.1967546 0.7943790
## 6:               AKT phosphorylates targets in the cytosol 0.9502982 0.9841196
##       log2err         ES        NES size leadingEdge
## 1: 0.14551615 -0.9081081 -1.2240437    1        3159
## 2: 0.04870109 -0.5445946 -0.7340619    1        5684
## 3: 0.09787733 -0.7175666 -1.1003535    2          23
## 4: 0.05490737 -0.4599280 -0.7839573    3        5576
## 5: 0.13959967  0.9054054  1.2137234    1        5321
## 6: 0.04773424 -0.4130288 -0.6333596    2         572
#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set1, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set1_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                    pathway       pval      padj
## 1:                                  2-LTR circle formation 0.07676768 0.9608552
## 2:                               ABC transporter disorders 0.61493124 0.9608552
## 3:                  ABC-family proteins mediated transport 0.39509954 0.9608552
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.70250896 0.9608552
## 5:               ADP signalling through P2Y purinoceptor 1 0.66797642 0.9608552
## 6:               AKT phosphorylates targets in the cytosol 0.38968481 0.9608552
##       log2err         ES        NES size leadingEdge
## 1: 0.23112671 -0.9648649 -1.2916120    1        3159
## 2: 0.06767604  0.6837838  0.9198979    1        5684
## 3: 0.07747675 -0.6551541 -1.0665815    2          23
## 4: 0.09255289  0.3980628  0.8110229    3   5573,5577
## 5: 0.06364241  0.6581081  0.8853563    1        5321
## 6: 0.11524000  0.6129905  1.0457381    2   572,84335
#data_results <- get_df_long(dep)

Session Info

sessionInfo()
## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] lubridate_1.9.2             forcats_1.0.0              
##  [3] stringr_1.5.0               dplyr_1.1.2                
##  [5] purrr_1.0.1                 readr_2.1.4                
##  [7] tidyr_1.3.0                 tibble_3.2.1               
##  [9] ggplot2_3.4.2               tidyverse_2.0.0            
## [11] mdatools_0.14.0             SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
## [15] MatrixGenerics_1.10.0       matrixStats_1.0.0          
## [17] DEP_1.20.0                  org.Hs.eg.db_3.16.0        
## [19] AnnotationDbi_1.60.2        IRanges_2.32.0             
## [21] S4Vectors_0.36.2            Biobase_2.58.0             
## [23] BiocGenerics_0.44.0         fgsea_1.24.0               
## 
## loaded via a namespace (and not attached):
##   [1] circlize_0.4.15        fastmatch_1.1-3        plyr_1.8.8            
##   [4] igraph_1.5.0.1         gmm_1.8                lazyeval_0.2.2        
##   [7] shinydashboard_0.7.2   crosstalk_1.2.0        BiocParallel_1.32.6   
##  [10] digest_0.6.33          foreach_1.5.2          htmltools_0.5.5       
##  [13] fansi_1.0.4            magrittr_2.0.3         memoise_2.0.1         
##  [16] cluster_2.1.4          doParallel_1.0.17      tzdb_0.4.0            
##  [19] limma_3.54.2           ComplexHeatmap_2.14.0  Biostrings_2.66.0     
##  [22] imputeLCMD_2.1         sandwich_3.0-2         timechange_0.2.0      
##  [25] colorspace_2.1-0       blob_1.2.4             xfun_0.39             
##  [28] crayon_1.5.2           RCurl_1.98-1.12        jsonlite_1.8.7        
##  [31] impute_1.72.3          zoo_1.8-12             iterators_1.0.14      
##  [34] glue_1.6.2             hash_2.2.6.2           gtable_0.3.3          
##  [37] zlibbioc_1.44.0        XVector_0.38.0         GetoptLong_1.0.5      
##  [40] DelayedArray_0.24.0    shape_1.4.6            scales_1.2.1          
##  [43] pheatmap_1.0.12        vsn_3.66.0             mvtnorm_1.2-2         
##  [46] DBI_1.1.3              Rcpp_1.0.11            plotrix_3.8-2         
##  [49] mzR_2.32.0             viridisLite_0.4.2      xtable_1.8-4          
##  [52] clue_0.3-64            reactome.db_1.82.0     bit_4.0.5             
##  [55] preprocessCore_1.60.2  sqldf_0.4-11           MsCoreUtils_1.10.0    
##  [58] DT_0.28                htmlwidgets_1.6.2      httr_1.4.6            
##  [61] gplots_3.1.3           RColorBrewer_1.1-3     ellipsis_0.3.2        
##  [64] farver_2.1.1           pkgconfig_2.0.3        XML_3.99-0.14         
##  [67] sass_0.4.7             utf8_1.2.3             STRINGdb_2.10.1       
##  [70] labeling_0.4.2         tidyselect_1.2.0       rlang_1.1.1           
##  [73] later_1.3.1            munsell_0.5.0          tools_4.2.3           
##  [76] cachem_1.0.8           cli_3.6.1              gsubfn_0.7            
##  [79] generics_0.1.3         RSQLite_2.3.1          fdrtool_1.2.17        
##  [82] evaluate_0.21          fastmap_1.1.1          mzID_1.36.0           
##  [85] yaml_2.3.7             knitr_1.43             bit64_4.0.5           
##  [88] caTools_1.18.2         KEGGREST_1.38.0        ncdf4_1.21            
##  [91] mime_0.12              compiler_4.2.3         rstudioapi_0.15.0     
##  [94] plotly_4.10.2          png_0.1-8              affyio_1.68.0         
##  [97] stringi_1.7.12         bslib_0.5.0            highr_0.10            
## [100] MSnbase_2.24.2         lattice_0.21-8         ProtGenerics_1.30.0   
## [103] Matrix_1.6-0           tmvtnorm_1.5           vctrs_0.6.3           
## [106] pillar_1.9.0           norm_1.0-11.1          lifecycle_1.0.3       
## [109] BiocManager_1.30.21.1  jquerylib_0.1.4        MALDIquant_1.22.1     
## [112] GlobalOptions_0.1.2    data.table_1.14.8      cowplot_1.1.1         
## [115] bitops_1.0-7           httpuv_1.6.11          R6_2.5.1              
## [118] pcaMethods_1.90.0      affy_1.76.0            promises_1.2.0.1      
## [121] KernSmooth_2.23-22     codetools_0.2-19       MASS_7.3-60           
## [124] gtools_3.9.4           assertthat_0.2.1       chron_2.3-61          
## [127] proto_1.0.0            rjson_0.2.21           withr_2.5.0           
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3         hms_1.1.3             
## [133] grid_4.2.3             rmarkdown_2.23         shiny_1.7.4.1
knitr::knit_exit()